NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database | Paper Citation Record

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This paper cites Is Your LLM Outdated? A Deep Look at Temporal Generalization.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Is Your LLM Outdated? A Deep Look at Temporal Generalization

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This paper cites Openagi: When llm meets domain experts.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Openagi: When llm meets domain experts

Reference 2

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This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 3

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This paper cites Does fine-tuning llms on new knowledge encourage hallucinations? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7765–7784, 2024.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Does fine-tuning llms on new knowledge encourage hallucinations? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7765–7784, 2024

Reference 4

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This paper cites Knowledge editing for large language models: A survey.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Knowledge editing for large language models: A survey

Reference 5

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Unresolved cited work

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This paper cites Can We Edit Factual Knowledge by In-Context Learning?.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Can We Edit Factual Knowledge by In-Context Learning?

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This paper cites Andonian, Yonatan Belinkov, and David Bau.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Andonian, Yonatan Belinkov, and David Bau

Reference 8

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This paper cites Locating and editing factual associations in GPT.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Locating and editing factual associations in GPT

Reference 9

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This paper cites PMET: precise model editing in a transformer.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database PMET: precise model editing in a transformer

Reference 10

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This paper cites Alphaedit: Null-space constrained model editing for language models.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Alphaedit: Null-space constrained model editing for language models

Reference 11

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This paper cites Transformer feed-forward layers are key-value memories.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Transformer feed-forward layers are key-value memories

Reference 12

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This paper cites Reasons and solutions for the decline in model performance after editing.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Reasons and solutions for the decline in model performance after editing

Reference 13

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This paper cites Language models are unsupervised multitask learners.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Language models are unsupervised multitask learners

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This paper cites Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Lan- guage Model with JAX.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Lan- guage Model with JAX

Reference 15

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This paper cites The Llama 3 Herd of Models.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database The Llama 3 Herd of Models

Reference 16

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This paper cites MELO: enhancing model editing with neuron- indexed dynamic lora.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database MELO: enhancing model editing with neuron- indexed dynamic lora

Reference 17

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This paper cites Editing large language models via adaptive gradient guidance.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Editing large language models via adaptive gradient guidance

Reference 18

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This paper cites Zero-shot relation extraction via reading comprehension.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Zero-shot relation extraction via reading comprehension

Reference 19

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This paper cites Liu, and Matt Gardner.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Liu, and Matt Gardner

Reference 20

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Measuring massive multitask language understanding

Reference 21

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This paper cites CommonsenseQA: A question answering challenge targeting commonsense knowledge.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database CommonsenseQA: A question answering challenge targeting commonsense knowledge

Reference 22

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 23

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This paper cites A surprisingly robust trick for the Winograd schema challenge.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database A surprisingly robust trick for the Winograd schema challenge

Reference 24

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This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 25

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This paper cites A framework for few-shot language model evaluation, 07 2024.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database A framework for few-shot language model evaluation, 07 2024

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This paper cites Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory

Reference 27

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This paper cites Should we really edit language models? on the evaluation of edited language models.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Should we really edit language models? on the evaluation of edited language models

Reference 28

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This paper cites Editing the mind of giants: An in-depth exploration of pitfalls of knowledge editing in large language models.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Editing the mind of giants: An in-depth exploration of pitfalls of knowledge editing in large language models

Reference 29

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Model editing harms general abilities of large language models: Regularization to the rescue

Reference 30

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Perturbation- restrained sequential model editing

Reference 31

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This paper cites Editing Factual Knowledge in Language Models.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Editing Factual Knowledge in Language Models

Reference 32

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Fast Model Editing at Scale

Reference 33

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database InstructEdit: Instruction-based Knowledge Editing for Large Language Models

Reference 34

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Memory-based model editing at scale

Reference 35

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This paper cites Transformer-Patcher: One Mistake worth One Neuron.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Transformer-Patcher: One Mistake worth One Neuron

Reference 36

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This paper cites Aging with grace: Lifelong model editing with discrete key-value adaptors.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Aging with grace: Lifelong model editing with discrete key-value adaptors

Reference 37

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Melo: Enhancing model editing with neuron- indexed dynamic lora

Reference 38

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This paper cites MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality

Reference 39

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This paper cites Memory-assisted prompt editing to improve GPT-3 after deployment.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Memory-assisted prompt editing to improve GPT-3 after deployment

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This paper cites Can we edit factual knowledge by in-context learning? In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Can we edit factual knowledge by in-context learning? In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Reference 41

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This paper cites MQuAKE: Assessing knowledge editing in language models via multi-hop questions.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database MQuAKE: Assessing knowledge editing in language models via multi-hop questions

Reference 42

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This paper cites PokeMQA: Programmable knowledge editing for multi-hop question answering.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database PokeMQA: Programmable knowledge editing for multi-hop question answering

Reference 43

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This paper cites Retrieval-enhanced knowledge editing in language models for multi-hop question an- swering.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Retrieval-enhanced knowledge editing in language models for multi-hop question an- swering

Reference 44

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database subject is a

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NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database Unresolved cited work

Reference 46

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This paper cites The boxplots are generated from the mean and variance of weight scores, with the center line indicating the mean, boxes showing ±1 standard deviation, and whiskers ±1.5.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database The boxplots are generated from the mean and variance of weight scores, with the center line indicating the mean, boxes showing ±1 standard deviation, and whiskers ±1.5

Reference 47

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This paper cites These results confirm that, during inference, residuals unrelated to the edited facts remain inactive, resulting in near-zero weighted scores.

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database These results confirm that, during inference, residuals unrelated to the edited facts remain inactive, resulting in near-zero weighted scores

Reference 48

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